Prediction of early recurrence and response to adjuvant Sorafenib for hepatocellular carcinoma after resection.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34900444", "kind": "science-record", "revision": 4, "content_hash": "sha256:e19e9d47d740b9c56f4418fa596878c82786d54972f77d50cbcaca71184a028d", "created_at": "2026-11-01T17:36:37Z", "updated_at": "2026-11-01T22:05:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prediction of early recurrence and response to adjuvant Sorafenib for hepatocellular carcinoma after resection.", "doi": "10.7717/peerj.12554", "pmid": "34900444", "authors": ["Liming Zheng", "Xi Gu", "Guojun Zheng", "Xin Li", "Meifang He", "Longgen Liu", "Xike Zhou"], "journal": "PeerJ", "year": 2021, "abstract": "BACKGROUND: Early recurrence of hepatocellular carcinoma (HCC) is a major obstacle to improving the prognosis, and no widely accepted adjuvant therapy guideline for patients post-liver resection is available. Currently, all available methods and biomarkers are insufficient to accurately predict post-operation HCC patients' risk of early recurrence and their response to adjuvant therapy.\nMETHODS: In this study, we downloaded four gene expression datasets (GSE14520, GSE54236, GSE87630, and GSE109211) from the Gene Expression Omnibus database and identified 34 common differentially expressed genes associated with HCC dysregulation and response to adjuvant sorafenib. Then, we constructed a novel 11-messenger RNA predictive model by using ROC curves analysis, univariate Cox regression analysis, and LASSO Cox regression analysis. Furthermore, we validated the predictive values of the risk model in GSE14520 and TCGA-LIHC cohorts by using Kaplan-Meier survival analysis, multivariable Cox regression analysis, and decision curve analysis, respectively.\nRESULTS: The risk score model could identify patients with a high risk of HCC recurrence at the early stage and could predict the response of patients to adjuvant sorafenib. Patients with a high risk score had a worse recurrence rate in training cohorts (2-year: p < 0.0001, hazard ratio (HR): 4.658, confidence interval 95% CI [2.895-7.495]; 5-year: p < 0.0001, HR: 3.251, 95% CI [2.155-4.904]) and external validation cohorts (2-year: p < 0.001, HR: 3.65, 95% CI [2.001-6.658]; 5-year: p < 0.001, HR: 3.156, 95% CI [1.78-5.596]). The AUC values of the risk score model for predicting tumor early recurrence were 0.746 and 0.618, and that of the risk score model for predicting the response to adjuvant sorafenib were 0.722 and 0.708 in the different cohort, respectively. Multivariable Cox regression analysis and decision curve analysis also showed that the risk score model was superior to and independent of other clinicopathologic characteristics. Moreover, the risk score model had excellent abilities to predict the overall survival and HCC recurrence of patients with the same tumor stage category.\nCONCLUSIONS: Our risk model is a reliable and superior predictive tool. With this model, we could optimize the risk stratification based on early tumor recurrence and could evaluate the response of patients to adjuvant sorafenib after liver resection.", "claims": [{"id": "claim-method", "statement": "Finally, these selected DEGs with the binary status were further analyzed using the least absolute shrinkage and selection operator (LASSO) Cox regression which could be used to construct risk models by screening variables in high dimension data (Gui & Li, 2005; Shi et al., 2017).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34900444", "locator": "sections[5] \"Establishment and validation of LASSO Cox regression model\" paragraph 2", "quote": "Finally, these selected DEGs with the binary status were further analyzed using the least absolute shrinkage and selection operator (LASSO) Cox regression which could be used to construct risk models by screening variables in high dimension data (Gui & Li, 2005; Shi et al., 2017)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data is available at NCBI GEO: GSE14520, GSE54236, GSE87630, and GSE109211. The TCGA-LIHC was the validation cohort downloaded from The Cancer Genome Atlas.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34900444", "locator": "sections[15] \"Additional Information and Declarations\" paragraph 4", "quote": "Data is available at NCBI GEO: GSE14520, GSE54236, GSE87630, and GSE109211. The TCGA-LIHC was the validation cohort downloaded from The Cancer Genome Atlas."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The 11-messenger RNA risk score identified patients at high risk of early HCC recurrence and predicted response to adjuvant sorafenib, with significantly higher recurrence risk in the high-risk groups of both discovery and external validation cohorts.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Patients with a high risk score had a worse recurrence rate in training cohorts (2-year: p < 0.0001, hazard ratio (HR): 4.658, confidence interval 95% CI [2.895–7.495]; 5-year: p < 0.0001, HR: 3.251, 95% CI [2.155–4.904]) and external validation cohorts (2-year: p < 0.001, HR: 3.65, 95% CI [2.001–6.658]; 5-year: p < 0.001, HR: 3.156, 95% CI [1.78–5.596])."}, "evidence": [{"asset": "source:pmid:34900444", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The risk score model could identify patients with a high risk of HCC recurrence at the early stage and could predict the response of patients to adjuvant sorafenib."}, {"asset": "source:pmid:34900444", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Patients with a high risk score had a worse recurrence rate in training cohorts (2-year: p < 0.0001, hazard ratio (HR): 4.658, confidence interval 95% CI [2.895–7.495]; 5-year: p < 0.0001, HR: 3.251, 95% CI [2.155–4.904]) and external validation cohorts (2-year: p < 0.001, HR: 3.65, 95% CI [2.001–6.658]; 5-year: p < 0.001, HR: 3.156, 95% CI [1.78–5.596])."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The 11-messenger RNA risk score identified patients at high risk of early HCC recurrence and predicted response to adjuvant sorafenib, with significantly higher recurrence risk in the high-risk groups of both discovery and external validation cohorts.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34900444"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE109211"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE109211"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34900444", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.12554", "sha256": "sha256:275afdf683177bbc97d4aa950d3d71ee49056cbdf251007a373926b84878c1e4", "bytes": 33948, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.12554", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8628622", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE109211", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109211", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE109211", "version": null, "note": "Data is available at NCBI GEO: GSE14520, GSE54236, GSE87630, and GSE109211. 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